Skip to main content

Surface nuclear magnetic resonance workbench

Project description

Akvo

Akvo provides processing of surface NMR data. It aims to be simple to use yet flexible for accommodating changes to processing flow. Akvo is written primarily in Python 3 with a small amount of R as well. The application is written around a Qt GUI with plotting provided by Matplotlib. NMR modelling and EM functionality is provided by Lemma (pyLemma).

The bleeding-edge code may be accessed using the git client located at either

git clone http://lemma.codes/LemmaSoftware/Akvo.git 

or, using our GitHub mirror

git clone https://github.com/LemmaSoftware/akvo.git  

Installation

Installation is straightforward. The only prerequisite that is sometimes not properly handled is PyQt5 which sometimes needs to be manually installed, i.e. pip install pyqt5

python3 setup.py build 
python3 setup.py install

Alternatively, release versions can be installed via pip

pip install akvo

Team

Akvo is developed by several teams including the University of Utah. If you would like to contribute, please send an email to info(at)lemmasoftware.org.

Capabilities

Akvo currently has preprocessing capabilities for VistaClara GMR data.

Benefits

Reproducibility

Processing steps are retained and logged in the processed file header, which is written in YAML. This allows data processing to be repeatable.

Open source

Akvo is truly open source, anyone can access, use, and change the source code.

Languages

Akvo is written primarily in Python 3. The graphical user interface is written in PyQt5. An interface to modelling software written in C++ (Lemma and Merlin) is in development.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Akvo-1.7.2.tar.gz (299.6 kB view details)

Uploaded Source

Built Distribution

Akvo-1.7.2-py3-none-any.whl (321.6 kB view details)

Uploaded Python 3

File details

Details for the file Akvo-1.7.2.tar.gz.

File metadata

  • Download URL: Akvo-1.7.2.tar.gz
  • Upload date:
  • Size: 299.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for Akvo-1.7.2.tar.gz
Algorithm Hash digest
SHA256 07ff2133cf1445735e480db66cc3f5b4277462ce7e93fa612d6809809e164bf0
MD5 f45de07a15c390e543b7196a056d0484
BLAKE2b-256 1576104a42987c238a68bab84aa08256b56e4c2595787059b7a8bf91a52c15ea

See more details on using hashes here.

File details

Details for the file Akvo-1.7.2-py3-none-any.whl.

File metadata

  • Download URL: Akvo-1.7.2-py3-none-any.whl
  • Upload date:
  • Size: 321.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for Akvo-1.7.2-py3-none-any.whl
Algorithm Hash digest
SHA256 064c4a759f19645b9d128336f5be76dfb03e9671871a7d9353fc9b773ff2f61a
MD5 691a8cac8f324846e59a1410f6d065b1
BLAKE2b-256 7cfc05bfb2c04f53f0e1cf1956d963bd70c046fbc28e350aee1ffeea9d042361

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page